A synthetic neural system is an information processing paradigm that is inspired by the way biological neural systems, such as the brain, process information. Synthetic neural systems are non-biological systems.
A key element of synthetic neural systems is the general architecture of the synthetic neural system. A synthetic neural system is composed of a large number of highly interconnected processing elements that are analogous to neurons in a brain working in parallel to solve specific problems. Unlike general purpose brains, a synthetic neural system is typically configured for a specific application, such as pattern recognition or data classification.
Synthetic neural systems derive meaning from complicated or imprecise data and are used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained synthetic neural system can be thought of as an “expert” in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer “what if” questions.
Synthetic neural systems, like people, learn by example. The synthetic neural systems are adapted, changed and reconfigured through a learning process in which results are compared to goals and objectives, and changes are made to the synthetic neural system to hopefully conform future results of the synthetic neural system to the goals and objectives. Learning in both biological systems and synthetic neural systems involves adjustments to connections between the neurons.
Conventional synthetic neural systems have three fundamental problems that prevent such systems from adequately functioning in a manner similar to natural biological neural systems. The first problem of conventional synthetic neural systems is the inability to reconcile the three dimensional nature of biological neural systems with the two dimensional nature of microprocessor technologies. The second problem is the inability of conventional synthetic neural systems to actively rewire themselves in response to changing requirements, as do biological neural systems. The third problem with conventional synthetic neural systems is the inability to perform significant tasks with complete autonomy. Synthetic neural systems advantageously capture all of these characteristics to enable successful independent operation.
For the reasons stated above, and for other reasons stated below that will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for a synthetic neural system that reconciles the two dimensional nature of microprocessor technologies to the three dimensional nature of biological neural systems. There is also a need for a synthetic neural system that adapts itself to changing external requirements. There is a further need for a synthetic neural system that performs significant tasks with complete autonomy.